class_docstr: HRFPN (High Resolution Feature Pyramids)
  
  paper: `High-Resolution Representations for Labeling Pixels and Regions
  <https://arxiv.org/abs/1904.04514>`_.
  
  Args:
      in_channels (list): number of channels for each branch.
      out_channels (int): output channels of feature pyramids.
      num_outs (int): number of output stages.
      pooling_type (str): pooling for generating feature pyramids
          from {MAX, AVG}.
      conv_cfg (dict): dictionary to construct and config conv layer.
      norm_cfg (dict): dictionary to construct and config norm layer.
      with_cp  (bool): Use checkpoint or not. Using checkpoint will save some
          memory while slowing down the training speed.
      stride (int): stride of 3x3 convolutional layers
      init_cfg (dict or list[dict], optional): Initialization config dict.
docstr: Forward function.
docstr_tok: ["Forward","function","."]
code: def forward(self, inputs):
      """Forward function."""
      assert len(inputs) == self.num_ins
      outs = [inputs[0]]
      for i in range(1, self.num_ins):
          outs.append(
              F.interpolate(
                  inputs[i], scale_factor=2**i, mode="bilinear"
              )
          )
      out = torch.cat(outs, dim=1)
      if out.requires_grad and self.with_cp:
          out = checkpoint(self.reduction_conv, out)
      else:
          out = self.reduction_conv(out)
      outs = [out]
      for i in range(1, self.num_outs):
          outs.append(
              self.pooling(out, kernel_size=2**i, stride=2**i)
          )
      outputs = []
  
      for i in range(self.num_outs):
          if outs[i].requires_grad and self.with_cp:
              tmp_out = checkpoint(self.fpn_convs[i], outs[i])
          else:
              tmp_out = self.fpn_convs[i](outs[i])
          outputs.append(tmp_out)
      return tuple(outputs)
  
file_path: mmdetection/mmdet/models/necks/hrfpn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule
  from torch.utils.checkpoint import checkpoint
  from mmdet.registry import MODELS
label_desc: Use image node_id 2 for calling the HRFPN obj's underlying member method code with example usage: obj.forward(inputs) and returns: tuple